{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 光伏数据分析（以临沂蒙阴县为例）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2021/11/23 \n",
    "#张赛赛 \n",
    "#pv_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests                                                                      \n",
    "from bs4 import BeautifulSoup \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "流程;\n",
    "\n",
    "1. 过滤：判断当前日期的数据时间序列，5-20点的数据是否完整，必须使用完整的时间序列\n",
    "2. 过滤：读入一日所有的原始数据，使用蒙阴县的用户进行第二次过滤\n",
    "3. 过滤：过滤每天5点发电量很大（或者负数），23点发电量很小（为0或者很小）的数据异常用户\n",
    "4. 过滤：过滤有发电量无光伏容量，或者有容量无发电量的用户\n",
    "5. 过滤：根据正态分布 过滤离群的数据，填充空值数据\n",
    "6. 将符合1中序列要求的日期数据都进行上述过滤，按时间序列（每个用户）顺序合并。\n",
    "7. 处理天气数据，每日每小时的天气与用户数据进行合并\n",
    "8. 对台区，用户编号等字符embedding/onehot处理，对数据进行归一化处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总用户数: 310\n",
      "蒙阴县台区数: 34\n",
      "蒙阴县用户数: 192\n"
     ]
    }
   ],
   "source": [
    "#所有用户发电情况(311个)\n",
    "#with open(\"./gfdata/data/20211109/202111092300.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211110/202111101900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211111/202111111900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211112/202111122300.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211113/202111132200.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211114/202111141900.txt\",encoding='utf-8') as f:\n",
    "with open(\"./gfdata/data/20211115/202111151900.txt\",encoding='utf-8') as f:  \n",
    "    content = f.read().replace(\"\\n\",\"\")\n",
    "    content = content[0:-1]\n",
    "jsondata = '{\"res\":[' + content + ']}'\n",
    "json_data = json.loads(jsondata)\n",
    "print(\"总用户数:\",len(json_data['res']))\n",
    "\n",
    "# 获取临沂市下面的所有台区\n",
    "sbbm = np.loadtxt('./data/37413.txt', dtype = str)\n",
    "print(\"蒙阴县台区数:\",len(sbbm))\n",
    "\n",
    "#解析json\n",
    "#异常值处理\n",
    "#过滤台区\n",
    "gfdata = []\n",
    "index = 1\n",
    "for i in range(len(json_data['res'])):\n",
    "    data = dict()\n",
    "    data['sbbm'] = json_data['res'][i]['SBBM']\n",
    "    data['ycsb'] = json_data['res'][i]['result']['Data']['ycsb']\n",
    "    data['yhmc'] = json_data['res'][i]['result']['Data']['yhmc']\n",
    "    data['gfrl'] = float(json_data['res'][i]['result']['Data']['gfrl'])*5 if json_data['res'][i]['result']['Data']['gfrl']!=None else 0\n",
    "    data['drfdl'] =float(json_data['res'][i]['result']['Data']['drfdl']) if json_data['res'][i]['result']['Data']['drfdl']!=None else 0\n",
    "    if(data['sbbm'] in sbbm):\n",
    "#    if(data['sbbm'] == '06M00001200397653'):\n",
    "        #data['id'] = index\n",
    "        index = index + 1\n",
    "        gfdata.append(data)\n",
    "        \n",
    "#转为dataframe\n",
    "gfdata = pd.DataFrame(gfdata)\n",
    "print(\"蒙阴县用户数:\",len(gfdata))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sbbm</th>\n",
       "      <th>ycsb</th>\n",
       "      <th>yhmc</th>\n",
       "      <th>gfrl</th>\n",
       "      <th>drfdl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06M00000012190101</td>\n",
       "      <td>202106300442GF</td>\n",
       "      <td>赵洪阳</td>\n",
       "      <td>150.000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06M00000000508090</td>\n",
       "      <td>202106190091GF</td>\n",
       "      <td>石立玖</td>\n",
       "      <td>84.000</td>\n",
       "      <td>156.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06M00000028492666</td>\n",
       "      <td>202106160130GF</td>\n",
       "      <td>潘玉平</td>\n",
       "      <td>340.250</td>\n",
       "      <td>589.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060283GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>174.900</td>\n",
       "      <td>128.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06M00000028492666</td>\n",
       "      <td>202106160132GF</td>\n",
       "      <td>赵伟</td>\n",
       "      <td>64.800</td>\n",
       "      <td>85.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>06M00001200918964</td>\n",
       "      <td>202106030152GF</td>\n",
       "      <td>张斌</td>\n",
       "      <td>247.275</td>\n",
       "      <td>231.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>06M00001200836502</td>\n",
       "      <td>202106117927GF</td>\n",
       "      <td>类承妍</td>\n",
       "      <td>153.000</td>\n",
       "      <td>112.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>06M00000013500092</td>\n",
       "      <td>202106120088GF</td>\n",
       "      <td>吴昌利</td>\n",
       "      <td>97.200</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>06M00001200395936</td>\n",
       "      <td>202106190378GF</td>\n",
       "      <td>张茂青</td>\n",
       "      <td>85.425</td>\n",
       "      <td>146.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>06M00000025075231</td>\n",
       "      <td>202106160073GF</td>\n",
       "      <td>姜鹏</td>\n",
       "      <td>149.350</td>\n",
       "      <td>101.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  sbbm            ycsb yhmc     gfrl   drfdl\n",
       "0    06M00000012190101  202106300442GF  赵洪阳  150.000    0.00\n",
       "1    06M00000000508090  202106190091GF  石立玖   84.000  156.26\n",
       "2    06M00000028492666  202106160130GF  潘玉平  340.250  589.96\n",
       "3    06M00000035359934  202106060283GF  杨传友  174.900  128.77\n",
       "4    06M00000028492666  202106160132GF   赵伟   64.800   85.19\n",
       "..                 ...             ...  ...      ...     ...\n",
       "187  06M00001200918964  202106030152GF   张斌  247.275  231.73\n",
       "188  06M00001200836502  202106117927GF  类承妍  153.000  112.17\n",
       "189  06M00000013500092  202106120088GF  吴昌利   97.200    0.00\n",
       "190  06M00001200395936  202106190378GF  张茂青   85.425  146.50\n",
       "191  06M00000025075231  202106160073GF   姜鹏  149.350  101.24\n",
       "\n",
       "[192 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gfdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 异常值数据总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.有装机容量 无发电量 \n",
    "用户有装机容量，无发电量。或者时有时无。\n",
    "\n",
    "可能原因 \n",
    "\n",
    "1.用户光伏停用,数据库未即使清理。处理方法:丢弃\n",
    "\n",
    "2.硬件故障导致的一段时间数据异常。处理方法:关注近期是否恢复，不恢复就丢弃。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#台区06M00001200431746最近一周内均无发电量\n",
    "#用户数量\n",
    "len(gfdata[gfdata['sbbm']=='06M00001200431746'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sbbm</th>\n",
       "      <th>ycsb</th>\n",
       "      <th>yhmc</th>\n",
       "      <th>gfrl</th>\n",
       "      <th>drfdl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600016GGF</td>\n",
       "      <td>庞守永</td>\n",
       "      <td>57.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600015GGF</td>\n",
       "      <td>张斌</td>\n",
       "      <td>55.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600787GGF</td>\n",
       "      <td>季利刚</td>\n",
       "      <td>54.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20216300005GGF</td>\n",
       "      <td>王在华</td>\n",
       "      <td>118.900</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600166GGF</td>\n",
       "      <td>赵凤</td>\n",
       "      <td>86.625</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600789GGF</td>\n",
       "      <td>李杰</td>\n",
       "      <td>75.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600117GGF</td>\n",
       "      <td>任继乾</td>\n",
       "      <td>50.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20216300163GGF</td>\n",
       "      <td>王增海</td>\n",
       "      <td>82.650</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600072GGF</td>\n",
       "      <td>季立军</td>\n",
       "      <td>107.250</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>202106300169GF</td>\n",
       "      <td>周悦</td>\n",
       "      <td>99.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600780GGF</td>\n",
       "      <td>张美增</td>\n",
       "      <td>45.900</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600165GGF</td>\n",
       "      <td>王朋</td>\n",
       "      <td>67.200</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600059GGF</td>\n",
       "      <td>赵红星</td>\n",
       "      <td>75.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600783GGF</td>\n",
       "      <td>罗公玉</td>\n",
       "      <td>50.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600788GGF</td>\n",
       "      <td>徐志刚</td>\n",
       "      <td>106.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600172GGF</td>\n",
       "      <td>季利刚</td>\n",
       "      <td>210.250</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20216300782GGF</td>\n",
       "      <td>季付和</td>\n",
       "      <td>42.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>06M00001200431746</td>\n",
       "      <td>20210600607GGF</td>\n",
       "      <td>王兆德</td>\n",
       "      <td>148.200</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  sbbm            ycsb yhmc     gfrl  drfdl\n",
       "15   06M00001200431746  20210600016GGF  庞守永   57.000    0.0\n",
       "25   06M00001200431746  20210600015GGF   张斌   55.000    0.0\n",
       "26   06M00001200431746  20210600787GGF  季利刚   54.000    0.0\n",
       "43   06M00001200431746  20216300005GGF  王在华  118.900    0.0\n",
       "44   06M00001200431746  20210600166GGF   赵凤   86.625    0.0\n",
       "51   06M00001200431746  20210600789GGF   李杰   75.000    0.0\n",
       "68   06M00001200431746  20210600117GGF  任继乾   50.000    0.0\n",
       "69   06M00001200431746  20216300163GGF  王增海   82.650    0.0\n",
       "114  06M00001200431746  20210600072GGF  季立军  107.250    0.0\n",
       "123  06M00001200431746  202106300169GF   周悦   99.000    0.0\n",
       "132  06M00001200431746  20210600780GGF  张美增   45.900    0.0\n",
       "136  06M00001200431746  20210600165GGF   王朋   67.200    0.0\n",
       "151  06M00001200431746  20210600059GGF  赵红星   75.000    0.0\n",
       "155  06M00001200431746  20210600783GGF  罗公玉   50.000    0.0\n",
       "159  06M00001200431746  20210600788GGF  徐志刚  106.000    0.0\n",
       "171  06M00001200431746  20210600172GGF  季利刚  210.250    0.0\n",
       "180  06M00001200431746  20216300782GGF  季付和   42.000    0.0\n",
       "182  06M00001200431746  20210600607GGF  王兆德  148.200    0.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gfdata[gfdata['sbbm']=='06M00001200431746']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过一周数据观察，台区06M00001200431746发电一直为空，选择直接过滤。其他台区同样异常用户也进行过滤。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|日期| 台区06M00001200431746发电量情况|其他台区发电量为0用户数量| \n",
    "|:--:|:--:|:--:|\n",
    "|20211109|均为0| 21|\n",
    "|20211110|均为0| 16|\n",
    "|20211111|均为0| 15|\n",
    "|20211112|均为0| 15|\n",
    "|20211113|均为0| 16|\n",
    "|20211114|均为0| 15|\n",
    "|20211115|均为0| 16|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总用户数: 311\n",
      "过滤后蒙阴县台区数: 33\n",
      "过滤后蒙阴县用户数: 174\n"
     ]
    }
   ],
   "source": [
    "#所有用户发电情况(311个)\n",
    "with open(\"./gfdata/data/20211109/202111092300.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211110/202111101900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211111/202111111900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211112/202111122300.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211113/202111132200.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211114/202111141900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211115/202111151900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211112/202111122300.txt\",encoding='utf-8') as f:\n",
    "    content = f.read().replace(\"\\n\",\"\")\n",
    "    content = content[0:-1]\n",
    "jsondata = '{\"res\":[' + content + ']}'\n",
    "json_data = json.loads(jsondata)\n",
    "print(\"总用户数:\",len(json_data['res']))\n",
    "\n",
    "# 过滤后的台区\n",
    "sbbm = np.loadtxt('./data/37413_1.txt', dtype = str)\n",
    "print(\"过滤后蒙阴县台区数:\",len(sbbm))\n",
    "\n",
    "#解析json\n",
    "#异常值处理\n",
    "#过滤台区\n",
    "gfdata = []\n",
    "index = 1\n",
    "for i in range(len(json_data['res'])):\n",
    "    data = dict()\n",
    "    data['sbbm'] = json_data['res'][i]['SBBM']\n",
    "    data['ycsb'] = json_data['res'][i]['result']['Data']['ycsb']\n",
    "    data['yhmc'] = json_data['res'][i]['result']['Data']['yhmc']\n",
    "    data['gfrl'] = float(json_data['res'][i]['result']['Data']['gfrl']) if json_data['res'][i]['result']['Data']['gfrl']!=None else 0\n",
    "    data['drfdl'] =float(json_data['res'][i]['result']['Data']['drfdl']) if json_data['res'][i]['result']['Data']['drfdl']!=None else 0\n",
    "    if(data['sbbm'] in sbbm):\n",
    "#    if(data['sbbm'] == '06M00001200397653'):\n",
    "        #data['id'] = index\n",
    "        index = index + 1\n",
    "        gfdata.append(data)\n",
    "        \n",
    "#转为dataframe\n",
    "gfdata = pd.DataFrame(gfdata)\n",
    "print(\"过滤后蒙阴县用户数:\",len(gfdata))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.有发电量无光伏容量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#其他部分的用户发电异常\n",
    "len(gfdata[gfdata['drfdl']==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sbbm</th>\n",
       "      <th>ycsb</th>\n",
       "      <th>yhmc</th>\n",
       "      <th>gfrl</th>\n",
       "      <th>drfdl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>06M00000014199184</td>\n",
       "      <td>202106150309GF</td>\n",
       "      <td>公桂香</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>06M00000030856378</td>\n",
       "      <td>202106220533GF</td>\n",
       "      <td>贾见国</td>\n",
       "      <td>0.0</td>\n",
       "      <td>448.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>06M00000011019255</td>\n",
       "      <td>202106090201GF</td>\n",
       "      <td>任昌学</td>\n",
       "      <td>0.0</td>\n",
       "      <td>273.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>06M00000011019255</td>\n",
       "      <td>202106090188GF</td>\n",
       "      <td>徐娜</td>\n",
       "      <td>0.0</td>\n",
       "      <td>513.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>06M00000030856378</td>\n",
       "      <td>202106220635GF</td>\n",
       "      <td>房德省</td>\n",
       "      <td>0.0</td>\n",
       "      <td>345.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>06M00000011019255</td>\n",
       "      <td>202106090120GF</td>\n",
       "      <td>孙庆武</td>\n",
       "      <td>0.0</td>\n",
       "      <td>324.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>06M00000011019255</td>\n",
       "      <td>202106090196GF</td>\n",
       "      <td>蒙阴县桃墟镇农村集体三资委托代理服务中心</td>\n",
       "      <td>0.0</td>\n",
       "      <td>512.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>06M00000011019255</td>\n",
       "      <td>202106090193GF</td>\n",
       "      <td>宋汉亮</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>06M00000030943995</td>\n",
       "      <td>202106300600GF</td>\n",
       "      <td>候传富</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>06M00000011019255</td>\n",
       "      <td>202106090092GF</td>\n",
       "      <td>石增凤</td>\n",
       "      <td>0.0</td>\n",
       "      <td>296.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>06M00000030856378</td>\n",
       "      <td>202106220797GF</td>\n",
       "      <td>杨德青</td>\n",
       "      <td>0.0</td>\n",
       "      <td>358.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>06M00000014199184</td>\n",
       "      <td>202106160335GF</td>\n",
       "      <td>王友祥</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  sbbm            ycsb                  yhmc  gfrl   drfdl\n",
       "16   06M00000014199184  202106150309GF                   公桂香   0.0  137.02\n",
       "45   06M00000030856378  202106220533GF                   贾见国   0.0  448.70\n",
       "64   06M00000011019255  202106090201GF                   任昌学   0.0  273.89\n",
       "67   06M00000011019255  202106090188GF                    徐娜   0.0  513.53\n",
       "71   06M00000030856378  202106220635GF                   房德省   0.0  345.81\n",
       "77   06M00000011019255  202106090120GF                   孙庆武   0.0  324.97\n",
       "87   06M00000011019255  202106090196GF  蒙阴县桃墟镇农村集体三资委托代理服务中心   0.0  512.30\n",
       "119  06M00000011019255  202106090193GF                   宋汉亮   0.0  151.94\n",
       "124  06M00000030943995  202106300600GF                   候传富   0.0  111.61\n",
       "133  06M00000011019255  202106090092GF                   石增凤   0.0  296.67\n",
       "164  06M00000030856378  202106220797GF                   杨德青   0.0  358.68\n",
       "167  06M00000014199184  202106160335GF                   王友祥   0.0   77.11"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#光伏容量异常用户\n",
    "gfdata[gfdata['gfrl']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#加入黑名单\n",
    "test1 = gfdata[gfdata['drfdl']==0]\n",
    "test2 = gfdata[gfdata['gfrl']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1.to_csv('./data/用户黑名单.txt', mode='a', header=False)\n",
    "test2.to_csv('./data/用户黑名单.txt', mode='a', header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "black=pd.read_csv('./data/用户黑名单.txt')\n",
    "black_ = black['mdid'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(black_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "#黑名单过滤"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总用户数: 310\n",
      "过滤后蒙阴县台区数: 33\n",
      "过滤后蒙阴县用户数: 140\n"
     ]
    }
   ],
   "source": [
    "#所有用户发电情况(311个)\n",
    "#with open(\"./gfdata/data/20211109/202111092300.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211110/202111101900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211111/202111111900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211112/202111122300.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211113/202111132200.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211114/202111141900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211115/202111151900.txt\",encoding='utf-8') as f:\n",
    "with open(\"./gfdata/data/20211112/202111122300.txt\",encoding='utf-8') as f:\n",
    "    content = f.read().replace(\"\\n\",\"\")\n",
    "    content = content[0:-1]\n",
    "jsondata = '{\"res\":[' + content + ']}'\n",
    "json_data = json.loads(jsondata)\n",
    "print(\"总用户数:\",len(json_data['res']))\n",
    "\n",
    "# 过滤后的台区\n",
    "sbbm = np.loadtxt('./data/37413_1.txt', dtype = str)\n",
    "print(\"过滤后蒙阴县台区数:\",len(sbbm))\n",
    "\n",
    "#解析json\n",
    "#异常值处理\n",
    "#过滤台区\n",
    "gfdata = []\n",
    "index = 1\n",
    "for i in range(len(json_data['res'])):\n",
    "    data = dict()\n",
    "    data['sbbm'] = json_data['res'][i]['SBBM']\n",
    "    data['ycsb'] = json_data['res'][i]['result']['Data']['ycsb']\n",
    "    data['yhmc'] = json_data['res'][i]['result']['Data']['yhmc']\n",
    "    data['gfrl'] = float(json_data['res'][i]['result']['Data']['gfrl']) if json_data['res'][i]['result']['Data']['gfrl']!=None else 0\n",
    "    data['drfdl'] = float(json_data['res'][i]['result']['Data']['drfdl']) if json_data['res'][i]['result']['Data']['drfdl']!=None else 0\n",
    "    data['dwfd'] = data['drfdl']/data['gfrl'] if data['gfrl']!=0 else 0\n",
    "    if((data['sbbm'] in sbbm) and (data['ycsb'] not in black_.to_list())):\n",
    "        data['id'] = index\n",
    "        index = index + 1\n",
    "        gfdata.append(data)\n",
    "        \n",
    "#转为dataframe\n",
    "gfdata = pd.DataFrame(gfdata)\n",
    "print(\"过滤后蒙阴县用户数:\",len(gfdata))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.离群数据\n",
    "由于用户情况不同，逆变器转换效率不同，不容易发现装机容量和发电量的数值异常，但是在同一个区县，一段时间内天气情况基本相同，单位时间内发电量/装机容量的值不会相差太远，其数据分布基本符合为正态分布（或多峰的正太分布）。\n",
    "\n",
    "可能的异常原因：报装容量过少或过多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pvdata = pd.read_csv('./data/merger_data.csv')\n",
    "pvdata.insert(5,'fdgl',pvdata['drfdl']/pvdata['gfrl'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pvdata = pvdata[pvdata['hour']==20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\dateutil\\parser\\_parser.py:1218: UnknownTimezoneWarning: tzname GF identified but not understood.  Pass `tzinfos` argument in order to correctly return a timezone-aware datetime.  In a future version, this will raise an exception.\n",
      "  category=UnknownTimezoneWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18008f6c408>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 10800x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pvdata.plot(x='ycsb',y=['fdgl'],figsize=(150,3),kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "符合正态分布的异常值的判定就用最基本的方法: 平均值(mean)+- 3*标准差（STD）之外的任何值，即为异常值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>data_date</th>\n",
       "      <th>time</th>\n",
       "      <th>fdgl</th>\n",
       "      <th>hour</th>\n",
       "      <th>gfrl</th>\n",
       "      <th>drfdl</th>\n",
       "      <th>vol_a</th>\n",
       "      <th>vol_b</th>\n",
       "      <th>vol_c</th>\n",
       "      <th>...</th>\n",
       "      <th>cur_c</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>szgl</th>\n",
       "      <th>hgl</th>\n",
       "      <th>glys</th>\n",
       "      <th>low_tp</th>\n",
       "      <th>high_tp</th>\n",
       "      <th>avg_tp</th>\n",
       "      <th>wind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2.478000e+03</td>\n",
       "      <td>2.478000e+03</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1101.778854</td>\n",
       "      <td>2.021112e+09</td>\n",
       "      <td>2.021112e+07</td>\n",
       "      <td>10.870214</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.770337</td>\n",
       "      <td>235.817038</td>\n",
       "      <td>228.874899</td>\n",
       "      <td>226.508878</td>\n",
       "      <td>226.387853</td>\n",
       "      <td>...</td>\n",
       "      <td>1.902478</td>\n",
       "      <td>1.873539</td>\n",
       "      <td>1.990835</td>\n",
       "      <td>2.046182</td>\n",
       "      <td>0.816114</td>\n",
       "      <td>2.590831</td>\n",
       "      <td>2.870056</td>\n",
       "      <td>13.776836</td>\n",
       "      <td>8.323446</td>\n",
       "      <td>2.279661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>637.396933</td>\n",
       "      <td>6.092563e+02</td>\n",
       "      <td>6.092563e+00</td>\n",
       "      <td>45.862942</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.297211</td>\n",
       "      <td>875.192605</td>\n",
       "      <td>38.334889</td>\n",
       "      <td>40.112176</td>\n",
       "      <td>40.097807</td>\n",
       "      <td>...</td>\n",
       "      <td>40.124311</td>\n",
       "      <td>44.855308</td>\n",
       "      <td>44.839637</td>\n",
       "      <td>40.118014</td>\n",
       "      <td>0.189808</td>\n",
       "      <td>40.073538</td>\n",
       "      <td>3.917712</td>\n",
       "      <td>3.706403</td>\n",
       "      <td>3.690734</td>\n",
       "      <td>0.975620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.021111e+09</td>\n",
       "      <td>2.021111e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>210.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-28.420000</td>\n",
       "      <td>-1.160000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>543.000000</td>\n",
       "      <td>2.021112e+09</td>\n",
       "      <td>2.021112e+07</td>\n",
       "      <td>4.680534</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14.840000</td>\n",
       "      <td>89.497500</td>\n",
       "      <td>223.500000</td>\n",
       "      <td>223.100000</td>\n",
       "      <td>222.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1103.000000</td>\n",
       "      <td>2.021112e+09</td>\n",
       "      <td>2.021112e+07</td>\n",
       "      <td>9.562137</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.620000</td>\n",
       "      <td>165.530000</td>\n",
       "      <td>226.300000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>225.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1647.000000</td>\n",
       "      <td>2.021113e+09</td>\n",
       "      <td>2.021113e+07</td>\n",
       "      <td>12.705540</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.540000</td>\n",
       "      <td>282.090000</td>\n",
       "      <td>230.100000</td>\n",
       "      <td>229.900000</td>\n",
       "      <td>229.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2271.000000</td>\n",
       "      <td>2.021113e+09</td>\n",
       "      <td>2.021113e+07</td>\n",
       "      <td>1231.984127</td>\n",
       "      <td>20.0</td>\n",
       "      <td>68.050000</td>\n",
       "      <td>29267.270000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             index     data_date          time         fdgl    hour  \\\n",
       "count  2478.000000  2.478000e+03  2.478000e+03  2478.000000  2478.0   \n",
       "mean   1101.778854  2.021112e+09  2.021112e+07    10.870214    20.0   \n",
       "std     637.396933  6.092563e+02  6.092563e+00    45.862942     0.0   \n",
       "min      15.000000  2.021111e+09  2.021111e+07     0.000000    20.0   \n",
       "25%     543.000000  2.021112e+09  2.021112e+07     4.680534    20.0   \n",
       "50%    1103.000000  2.021112e+09  2.021112e+07     9.562137    20.0   \n",
       "75%    1647.000000  2.021113e+09  2.021113e+07    12.705540    20.0   \n",
       "max    2271.000000  2.021113e+09  2.021113e+07  1231.984127    20.0   \n",
       "\n",
       "              gfrl         drfdl        vol_a        vol_b        vol_c  ...  \\\n",
       "count  2478.000000   2478.000000  2478.000000  2478.000000  2478.000000  ...   \n",
       "mean     22.770337    235.817038   228.874899   226.508878   226.387853  ...   \n",
       "std      11.297211    875.192605    38.334889    40.112176    40.097807  ...   \n",
       "min       5.000000      0.000000   210.500000     0.000000     0.000000  ...   \n",
       "25%      14.840000     89.497500   223.500000   223.100000   222.900000  ...   \n",
       "50%      20.620000    165.530000   226.300000   226.000000   225.800000  ...   \n",
       "75%      27.540000    282.090000   230.100000   229.900000   229.500000  ...   \n",
       "max      68.050000  29267.270000   999.000000   999.000000   999.000000  ...   \n",
       "\n",
       "             cur_c            p            q         szgl          hgl  \\\n",
       "count  2478.000000  2478.000000  2478.000000  2478.000000  2478.000000   \n",
       "mean      1.902478     1.873539     1.990835     2.046182     0.816114   \n",
       "std      40.124311    44.855308    44.839637    40.118014     0.189808   \n",
       "min       0.000000   -28.420000    -1.160000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.730000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.870000   \n",
       "75%       0.080000     0.000000     0.000000     0.190000     0.970000   \n",
       "max     999.000000   999.000000   999.000000   999.000000     1.000000   \n",
       "\n",
       "              glys       low_tp      high_tp       avg_tp         wind  \n",
       "count  2478.000000  2478.000000  2478.000000  2478.000000  2478.000000  \n",
       "mean      2.590831     2.870056    13.776836     8.323446     2.279661  \n",
       "std      40.073538     3.917712     3.706403     3.690734     0.975620  \n",
       "min       0.000000    -5.000000     5.000000     0.000000     1.500000  \n",
       "25%       1.000000     1.000000    13.000000     6.500000     1.500000  \n",
       "50%       1.000000     3.000000    15.000000     9.000000     1.500000  \n",
       "75%       1.000000     6.000000    17.000000    10.500000     3.500000  \n",
       "max     999.000000    10.000000    19.000000    14.000000     3.500000  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvdata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-126.71861199999998, 148.45904]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mstd = [10.870214-3*45.862942,10.870214+3*45.862942]\n",
    "mstd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sbbm</th>\n",
       "      <th>ycsb</th>\n",
       "      <th>data_date</th>\n",
       "      <th>time</th>\n",
       "      <th>fdgl</th>\n",
       "      <th>yhmc</th>\n",
       "      <th>hour</th>\n",
       "      <th>gfrl</th>\n",
       "      <th>drfdl</th>\n",
       "      <th>...</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>szgl</th>\n",
       "      <th>hgl</th>\n",
       "      <th>glys</th>\n",
       "      <th>low_tp</th>\n",
       "      <th>high_tp</th>\n",
       "      <th>avg_tp</th>\n",
       "      <th>weather</th>\n",
       "      <th>wind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15759</th>\n",
       "      <td>719</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021111020</td>\n",
       "      <td>20211110</td>\n",
       "      <td>1191.639394</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>11797.23</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>8.0</td>\n",
       "      <td>晴</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15775</th>\n",
       "      <td>879</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021111320</td>\n",
       "      <td>20211113</td>\n",
       "      <td>13.056566</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>129.26</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>9.5</td>\n",
       "      <td>晴</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15791</th>\n",
       "      <td>895</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021111520</td>\n",
       "      <td>20211115</td>\n",
       "      <td>9.416162</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>93.22</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>10.5</td>\n",
       "      <td>多云</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15807</th>\n",
       "      <td>943</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021111720</td>\n",
       "      <td>20211117</td>\n",
       "      <td>6.607071</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>65.41</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>12.0</td>\n",
       "      <td>多云</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15823</th>\n",
       "      <td>943</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021111820</td>\n",
       "      <td>20211118</td>\n",
       "      <td>11.697980</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>115.81</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>12.5</td>\n",
       "      <td>晴</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15839</th>\n",
       "      <td>895</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021111920</td>\n",
       "      <td>20211119</td>\n",
       "      <td>10.153535</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>100.52</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>14.0</td>\n",
       "      <td>阴</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15855</th>\n",
       "      <td>895</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021112020</td>\n",
       "      <td>20211120</td>\n",
       "      <td>4.061616</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>40.21</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>13.5</td>\n",
       "      <td>阴</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15871</th>\n",
       "      <td>879</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021112120</td>\n",
       "      <td>20211121</td>\n",
       "      <td>3.867677</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>38.29</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>7.0</td>\n",
       "      <td>阴</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15887</th>\n",
       "      <td>863</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021112220</td>\n",
       "      <td>20211122</td>\n",
       "      <td>14.433333</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>142.89</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>晴</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15903</th>\n",
       "      <td>895</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021112320</td>\n",
       "      <td>20211123</td>\n",
       "      <td>13.427273</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>132.93</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2</td>\n",
       "      <td>9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>晴</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15919</th>\n",
       "      <td>895</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021112420</td>\n",
       "      <td>20211124</td>\n",
       "      <td>12.749495</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>126.22</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>6.5</td>\n",
       "      <td>多云</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15935</th>\n",
       "      <td>895</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021112520</td>\n",
       "      <td>20211125</td>\n",
       "      <td>12.120202</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>119.99</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>8.0</td>\n",
       "      <td>晴</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15951</th>\n",
       "      <td>895</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021112620</td>\n",
       "      <td>20211126</td>\n",
       "      <td>11.942424</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>118.23</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>8.5</td>\n",
       "      <td>晴</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15967</th>\n",
       "      <td>895</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021112720</td>\n",
       "      <td>20211127</td>\n",
       "      <td>11.942424</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>118.23</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>9.0</td>\n",
       "      <td>多云</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15983</th>\n",
       "      <td>895</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021112820</td>\n",
       "      <td>20211128</td>\n",
       "      <td>11.942424</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>118.23</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>10.0</td>\n",
       "      <td>雨</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15999</th>\n",
       "      <td>895</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021112920</td>\n",
       "      <td>20211129</td>\n",
       "      <td>6.222222</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>61.60</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>9.0</td>\n",
       "      <td>雨</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16015</th>\n",
       "      <td>879</td>\n",
       "      <td>06M00000016438924</td>\n",
       "      <td>202106160265GF</td>\n",
       "      <td>2021113020</td>\n",
       "      <td>20211130</td>\n",
       "      <td>12.760606</td>\n",
       "      <td>龚圆圆</td>\n",
       "      <td>20</td>\n",
       "      <td>9.9</td>\n",
       "      <td>126.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-3</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>多云</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index               sbbm            ycsb   data_date      time  \\\n",
       "15759    719  06M00000016438924  202106160265GF  2021111020  20211110   \n",
       "15775    879  06M00000016438924  202106160265GF  2021111320  20211113   \n",
       "15791    895  06M00000016438924  202106160265GF  2021111520  20211115   \n",
       "15807    943  06M00000016438924  202106160265GF  2021111720  20211117   \n",
       "15823    943  06M00000016438924  202106160265GF  2021111820  20211118   \n",
       "15839    895  06M00000016438924  202106160265GF  2021111920  20211119   \n",
       "15855    895  06M00000016438924  202106160265GF  2021112020  20211120   \n",
       "15871    879  06M00000016438924  202106160265GF  2021112120  20211121   \n",
       "15887    863  06M00000016438924  202106160265GF  2021112220  20211122   \n",
       "15903    895  06M00000016438924  202106160265GF  2021112320  20211123   \n",
       "15919    895  06M00000016438924  202106160265GF  2021112420  20211124   \n",
       "15935    895  06M00000016438924  202106160265GF  2021112520  20211125   \n",
       "15951    895  06M00000016438924  202106160265GF  2021112620  20211126   \n",
       "15967    895  06M00000016438924  202106160265GF  2021112720  20211127   \n",
       "15983    895  06M00000016438924  202106160265GF  2021112820  20211128   \n",
       "15999    895  06M00000016438924  202106160265GF  2021112920  20211129   \n",
       "16015    879  06M00000016438924  202106160265GF  2021113020  20211130   \n",
       "\n",
       "              fdgl yhmc  hour  gfrl     drfdl  ...    p    q  szgl   hgl  \\\n",
       "15759  1191.639394  龚圆圆    20   9.9  11797.23  ...  0.0  0.0   0.0  0.75   \n",
       "15775    13.056566  龚圆圆    20   9.9    129.26  ...  0.0  0.0   0.0  0.77   \n",
       "15791     9.416162  龚圆圆    20   9.9     93.22  ...  0.0  0.0   0.0  0.86   \n",
       "15807     6.607071  龚圆圆    20   9.9     65.41  ...  0.0  0.0   0.0  0.90   \n",
       "15823    11.697980  龚圆圆    20   9.9    115.81  ...  0.0  0.0   0.0  0.92   \n",
       "15839    10.153535  龚圆圆    20   9.9    100.52  ...  0.0  0.0   0.0  0.84   \n",
       "15855     4.061616  龚圆圆    20   9.9     40.21  ...  0.0  0.0   0.0  0.95   \n",
       "15871     3.867677  龚圆圆    20   9.9     38.29  ...  0.0  0.0   0.0  0.97   \n",
       "15887    14.433333  龚圆圆    20   9.9    142.89  ...  0.0  0.0   0.0  0.82   \n",
       "15903    13.427273  龚圆圆    20   9.9    132.93  ...  0.0  0.0   0.0  0.83   \n",
       "15919    12.749495  龚圆圆    20   9.9    126.22  ...  0.0  0.0   0.0  0.84   \n",
       "15935    12.120202  龚圆圆    20   9.9    119.99  ...  0.0  0.0   0.0  0.83   \n",
       "15951    11.942424  龚圆圆    20   9.9    118.23  ...  0.0  0.0   0.0  0.86   \n",
       "15967    11.942424  龚圆圆    20   9.9    118.23  ...  0.0  0.0   0.0  0.86   \n",
       "15983    11.942424  龚圆圆    20   9.9    118.23  ...  0.0  0.0   0.0  0.86   \n",
       "15999     6.222222  龚圆圆    20   9.9     61.60  ...  0.0  0.0   0.0  0.93   \n",
       "16015    12.760606  龚圆圆    20   9.9    126.33  ...  0.0  0.0   0.0  0.85   \n",
       "\n",
       "       glys  low_tp  high_tp  avg_tp  weather  wind  \n",
       "15759   1.0       3       13     8.0        晴   1.5  \n",
       "15775   1.0       3       16     9.5        晴   1.5  \n",
       "15791   1.0       4       17    10.5       多云   3.5  \n",
       "15807   1.0       7       17    12.0       多云   1.5  \n",
       "15823   1.0       6       19    12.5        晴   1.5  \n",
       "15839   1.0      10       18    14.0        阴   1.5  \n",
       "15855   1.0      10       17    13.5        阴   1.5  \n",
       "15871   1.0       1       13     7.0        阴   3.5  \n",
       "15887   1.0      -5        5     0.0        晴   1.5  \n",
       "15903   1.0      -2        9     3.5        晴   3.5  \n",
       "15919   1.0       0       13     6.5       多云   3.5  \n",
       "15935   1.0       2       14     8.0        晴   1.5  \n",
       "15951   1.0       2       15     8.5        晴   1.5  \n",
       "15967   1.0       3       15     9.0       多云   1.5  \n",
       "15983   1.0       6       14    10.0        雨   3.5  \n",
       "15999   1.0       3       15     9.0        雨   1.5  \n",
       "16015   1.0      -3        7     2.0       多云   3.5  \n",
       "\n",
       "[17 rows x 26 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvdata[pvdata['ycsb']=='202106160265GF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>sbbm</th>\n",
       "      <th>ycsb</th>\n",
       "      <th>data_date</th>\n",
       "      <th>time</th>\n",
       "      <th>fdgl</th>\n",
       "      <th>yhmc</th>\n",
       "      <th>hour</th>\n",
       "      <th>gfrl</th>\n",
       "      <th>drfdl</th>\n",
       "      <th>...</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>szgl</th>\n",
       "      <th>hgl</th>\n",
       "      <th>glys</th>\n",
       "      <th>low_tp</th>\n",
       "      <th>high_tp</th>\n",
       "      <th>avg_tp</th>\n",
       "      <th>weather</th>\n",
       "      <th>wind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [index, sbbm, ycsb, data_date, time, fdgl, yhmc, hour, gfrl, drfdl, vol_a, vol_b, vol_c, cur_a, cur_b, cur_c, p, q, szgl, hgl, glys, low_tp, high_tp, avg_tp, weather, wind]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 26 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvdata[pvdata['fdgl']>148]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18aa9bb8288>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 5760x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filter1 = pvdata[pvdata['fdgl']<=25]\n",
    "#ztfb = filter1[filter1['dwfd']>4.339]\n",
    "filter1.plot(x='ycsb',y=['fdgl'],figsize=(80,3),kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18aaa7dd288>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pvdata.plot(x='ycsb',y=['fdgl'],figsize=(10,3),kind='density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sbbm</th>\n",
       "      <th>ycsb</th>\n",
       "      <th>yhmc</th>\n",
       "      <th>gfrl</th>\n",
       "      <th>drfdl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>06M00000035359934</td>\n",
       "      <td>202106060285GF</td>\n",
       "      <td>杨传友</td>\n",
       "      <td>14.84</td>\n",
       "      <td>469.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>06M00000010651458</td>\n",
       "      <td>202106160047GF</td>\n",
       "      <td>王新</td>\n",
       "      <td>7.80</td>\n",
       "      <td>229.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  sbbm            ycsb yhmc   gfrl   drfdl\n",
       "8    06M00000035359934  202106060285GF  杨传友  14.84  469.07\n",
       "115  06M00000010651458  202106160047GF   王新   7.80  229.22"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test3 = gfdata[gfdata['dwfd']>26.539665].iloc[:,0:5]\n",
    "test3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test4 = gfdata[gfdata['dwfd']<4.339].iloc[:,0:5]\n",
    "#test4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "test3.to_csv('./data/用户黑名单.txt', mode='a', header=False)\n",
    "#test4.to_csv('./data/用户黑名单.txt', mode='a', header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总用户数: 310\n",
      "过滤后蒙阴县台区数: 33\n",
      "过滤后蒙阴县用户数: 138\n"
     ]
    }
   ],
   "source": [
    "black=pd.read_csv('./data/用户黑名单.txt')\n",
    "black_ = black['mdid'].drop_duplicates()\n",
    "#所有用户发电情况(311个)\n",
    "#with open(\"./gfdata/data/20211109/202111092300.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211110/202111101900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211111/202111111900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211112/202111122300.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211113/202111132200.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211114/202111141900.txt\",encoding='utf-8') as f:\n",
    "#with open(\"./gfdata/data/20211115/202111151900.txt\",encoding='utf-8') as f:\n",
    "with open(\"./gfdata/data/20211112/202111122300.txt\",encoding='utf-8') as f:\n",
    "    content = f.read().replace(\"\\n\",\"\")\n",
    "    content = content[0:-1]\n",
    "jsondata = '{\"res\":[' + content + ']}'\n",
    "json_data = json.loads(jsondata)\n",
    "print(\"总用户数:\",len(json_data['res']))\n",
    "\n",
    "# 过滤后的台区\n",
    "sbbm = np.loadtxt('./data/37413_1.txt', dtype = str)\n",
    "print(\"过滤后蒙阴县台区数:\",len(sbbm))\n",
    "\n",
    "#解析json\n",
    "#异常值处理\n",
    "#过滤台区\n",
    "gfdata = []\n",
    "index = 1\n",
    "for i in range(len(json_data['res'])):\n",
    "    data = dict()\n",
    "    data['sbbm'] = json_data['res'][i]['SBBM']\n",
    "    data['ycsb'] = json_data['res'][i]['result']['Data']['ycsb']\n",
    "    data['yhmc'] = json_data['res'][i]['result']['Data']['yhmc']\n",
    "    data['gfrl'] = float(json_data['res'][i]['result']['Data']['gfrl'])*10 if json_data['res'][i]['result']['Data']['gfrl']!=None else 0\n",
    "    data['drfdl'] = float(json_data['res'][i]['result']['Data']['drfdl']) if json_data['res'][i]['result']['Data']['drfdl']!=None else 0\n",
    "    data['dwfd'] = data['drfdl']/data['gfrl'] if data['gfrl']!=0 else 0\n",
    "    if((data['sbbm'] in sbbm) and (data['ycsb'] not in black_.to_list())):\n",
    "        data['id'] = index\n",
    "        index = index + 1\n",
    "        gfdata.append(data)\n",
    "        \n",
    "#转为dataframe\n",
    "gfdata = pd.DataFrame(gfdata)\n",
    "print(\"过滤后蒙阴县用户数:\",len(gfdata))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='id', ylabel='[dwfd]'>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfdata.plot(x='id',y=['dwfd'],figsize=(15,3),kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfdata.plot(x='id',y=['dwfd'],figsize=(15,3),kind='density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='id'>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfdata.plot(x='id',y=['gfrl','drfdl'],figsize=(18,3),kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "读取: ../gfdata/data/20211109/202111090100.txt\n",
      "读取: ../gfdata/data/20211109/202111090200.txt\n",
      "读取: ../gfdata/data/20211109/202111090300.txt\n",
      "读取: ../gfdata/data/20211109/202111090400.txt\n",
      "读取: ../gfdata/data/20211109/202111090500.txt\n",
      "读取: ../gfdata/data/20211109/202111090600.txt\n",
      "读取: ../gfdata/data/20211109/202111090700.txt\n",
      "读取: ../gfdata/data/20211109/202111090800.txt\n",
      "读取: ../gfdata/data/20211109/202111090900.txt\n",
      "读取: ../gfdata/data/20211109/202111091000.txt\n",
      "读取: ../gfdata/data/20211109/202111091100.txt\n",
      "读取: ../gfdata/data/20211109/202111091200.txt\n",
      "读取: ../gfdata/data/20211109/202111091300.txt\n",
      "读取: ../gfdata/data/20211109/202111091400.txt\n",
      "读取: ../gfdata/data/20211109/202111091500.txt\n",
      "读取: ../gfdata/data/20211109/202111091600.txt\n",
      "读取: ../gfdata/data/20211109/202111091700.txt\n",
      "读取: ../gfdata/data/20211109/202111091800.txt\n",
      "读取: ../gfdata/data/20211109/202111091900.txt\n",
      "读取: ../gfdata/data/20211109/202111092000.txt\n",
      "读取: ../gfdata/data/20211109/202111092100.txt\n",
      "读取: ../gfdata/data/20211109/202111092200.txt\n",
      "读取: ../gfdata/data/20211109/202111092300.txt\n"
     ]
    }
   ],
   "source": [
    "content = \"\"\n",
    "for i in range(1,24):\n",
    "    if i < 10:\n",
    "        path = \"../gfdata/data/20211109/202111090\" + str(i) + \"00.txt\"\n",
    "    else:\n",
    "        path = \"../gfdata/data/20211109/20211109\" + str(i) + \"00.txt\"\n",
    "    print(\"读取:\",path)\n",
    "    with open(path,encoding='utf-8') as f:\n",
    "        content = content + f.read().replace(\"\\n\",\"\")[0:-1] + \",\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总用户数: 7153\n"
     ]
    }
   ],
   "source": [
    "content = content[0:-1]\n",
    "jsondata = '{\"res\":[' + content + ']}'\n",
    "json_data = json.loads(jsondata)\n",
    "print(\"总用户数:\",len(json_data['res']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gfdata = []\n",
    "index = 1\n",
    "for i in range(len(json_data['res'])):\n",
    "    data = dict()\n",
    "    data['sbbm'] = json_data['res'][i]['SBBM']\n",
    "    data['ycsb'] = json_data['res'][i]['result']['Data']['ycsb']\n",
    "    data['yhmc'] = json_data['res'][i]['result']['Data']['yhmc']\n",
    "    \n",
    "    data['p'] = (-1)*float(json_data['res'][i]['result']['Data']['p']) if json_data['res'][i]['result']['Data']['p']!=None else 0\n",
    "    data['q'] = float(json_data['res'][i]['result']['Data']['q']) if json_data['res'][i]['result']['Data']['q']!=None else 0\n",
    "    data['szgl'] = (2)*float(json_data['res'][i]['result']['Data']['szgl']) if json_data['res'][i]['result']['Data']['szgl']!=None else 0\n",
    "    \n",
    "    \n",
    "    data['glys'] = json_data['res'][i]['result']['Data']['glys']\n",
    "    data['gfrl'] = float(json_data['res'][i]['result']['Data']['gfrl']) if json_data['res'][i]['result']['Data']['gfrl']!=None else 0\n",
    "    data['drfdl'] =float(json_data['res'][i]['result']['Data']['drfdl']) if json_data['res'][i]['result']['Data']['drfdl']!=None else 0\n",
    "    #if(data['sbbm'] in sbbm):\n",
    "    if(data['ycsb'] == '202106120042GF'):\n",
    "        data['id'] = index\n",
    "        index = index + 1\n",
    "        gfdata.append(data)\n",
    "        \n",
    "#转为dataframe\n",
    "gfdata = pd.DataFrame(gfdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-dda6c2fce7f0>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gfdata['fdgl'][22] = 0\n",
      "<ipython-input-5-dda6c2fce7f0>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gfdata['fdgl'][0] = gfdata['drfdl'][i] - c\n",
      "<ipython-input-5-dda6c2fce7f0>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gfdata['fdgl'][i-1] = (gfdata['drfdl'][i] - gfdata['drfdl'][i-1])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sbbm</th>\n",
       "      <th>ycsb</th>\n",
       "      <th>yhmc</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>szgl</th>\n",
       "      <th>glys</th>\n",
       "      <th>gfrl</th>\n",
       "      <th>drfdl</th>\n",
       "      <th>id</th>\n",
       "      <th>fdgl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>6.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>5.93</td>\n",
       "      <td>0.18</td>\n",
       "      <td>11.86</td>\n",
       "      <td>0.99</td>\n",
       "      <td>21.1</td>\n",
       "      <td>6.47</td>\n",
       "      <td>8</td>\n",
       "      <td>26.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>11.81</td>\n",
       "      <td>0.22</td>\n",
       "      <td>23.62</td>\n",
       "      <td>0.99</td>\n",
       "      <td>21.1</td>\n",
       "      <td>32.72</td>\n",
       "      <td>9</td>\n",
       "      <td>40.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>15.39</td>\n",
       "      <td>0.15</td>\n",
       "      <td>30.78</td>\n",
       "      <td>0.99</td>\n",
       "      <td>21.1</td>\n",
       "      <td>73.44</td>\n",
       "      <td>10</td>\n",
       "      <td>45.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>17.23</td>\n",
       "      <td>0.26</td>\n",
       "      <td>34.46</td>\n",
       "      <td>0.99</td>\n",
       "      <td>21.1</td>\n",
       "      <td>119.04</td>\n",
       "      <td>11</td>\n",
       "      <td>51.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>16.70</td>\n",
       "      <td>0.16</td>\n",
       "      <td>33.40</td>\n",
       "      <td>0.99</td>\n",
       "      <td>21.1</td>\n",
       "      <td>170.75</td>\n",
       "      <td>12</td>\n",
       "      <td>52.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>15.07</td>\n",
       "      <td>0.17</td>\n",
       "      <td>30.14</td>\n",
       "      <td>0.99</td>\n",
       "      <td>21.1</td>\n",
       "      <td>223.35</td>\n",
       "      <td>13</td>\n",
       "      <td>40.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>11.75</td>\n",
       "      <td>0.16</td>\n",
       "      <td>23.50</td>\n",
       "      <td>0.99</td>\n",
       "      <td>21.1</td>\n",
       "      <td>263.83</td>\n",
       "      <td>14</td>\n",
       "      <td>5.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>11.21</td>\n",
       "      <td>0.15</td>\n",
       "      <td>22.44</td>\n",
       "      <td>0.99</td>\n",
       "      <td>21.1</td>\n",
       "      <td>269.60</td>\n",
       "      <td>15</td>\n",
       "      <td>24.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>294.42</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>294.42</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>294.42</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>294.42</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>294.42</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>294.42</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>294.42</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>294.42</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sbbm            ycsb yhmc      p     q   szgl  glys  gfrl  \\\n",
       "0   06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "1   06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "2   06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "3   06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "4   06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "5   06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "6   06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "7   06M00001200397653  202106120042GF  包培国   5.93  0.18  11.86  0.99  21.1   \n",
       "8   06M00001200397653  202106120042GF  包培国  11.81  0.22  23.62  0.99  21.1   \n",
       "9   06M00001200397653  202106120042GF  包培国  15.39  0.15  30.78  0.99  21.1   \n",
       "10  06M00001200397653  202106120042GF  包培国  17.23  0.26  34.46  0.99  21.1   \n",
       "11  06M00001200397653  202106120042GF  包培国  16.70  0.16  33.40  0.99  21.1   \n",
       "12  06M00001200397653  202106120042GF  包培国  15.07  0.17  30.14  0.99  21.1   \n",
       "13  06M00001200397653  202106120042GF  包培国  11.75  0.16  23.50  0.99  21.1   \n",
       "14  06M00001200397653  202106120042GF  包培国  11.21  0.15  22.44  0.99  21.1   \n",
       "15  06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "16  06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "17  06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "18  06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "19  06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "20  06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "21  06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "22  06M00001200397653  202106120042GF  包培国  -0.00  0.00   0.00  1.00  21.1   \n",
       "\n",
       "     drfdl  id   fdgl  \n",
       "0     0.00   1      0  \n",
       "1     0.00   2      0  \n",
       "2     0.00   3      0  \n",
       "3     0.00   4      0  \n",
       "4     0.00   5      0  \n",
       "5     0.00   6      0  \n",
       "6     0.00   7   6.47  \n",
       "7     6.47   8  26.25  \n",
       "8    32.72   9  40.72  \n",
       "9    73.44  10   45.6  \n",
       "10  119.04  11  51.71  \n",
       "11  170.75  12   52.6  \n",
       "12  223.35  13  40.48  \n",
       "13  263.83  14   5.77  \n",
       "14  269.60  15  24.82  \n",
       "15  294.42  16      0  \n",
       "16  294.42  17      0  \n",
       "17  294.42  18      0  \n",
       "18  294.42  19      0  \n",
       "19  294.42  20      0  \n",
       "20  294.42  21      0  \n",
       "21  294.42  22      0  \n",
       "22  294.42  23      0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = 0\n",
    "gfdata['fdgl'] = ''\n",
    "gfdata['fdgl'][22] = 0\n",
    "for i in range(0,23):\n",
    "    if i == 0:\n",
    "        gfdata['fdgl'][0] = gfdata['drfdl'][i] - c\n",
    "    else:\n",
    "        gfdata['fdgl'][i-1] = (gfdata['drfdl'][i] - gfdata['drfdl'][i-1])\n",
    "gfdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='id'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfdata.plot(x='id',y=['drfdl'],figsize=(20,3),kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='id'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfdata.plot(x='id',y=['szgl','p','q','fdgl'],figsize=(10,3),kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 天气因素分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#临沂历史天气\n",
    "his_weather = pd.read_csv('./data/weather.txt',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "爬取2021年11月临沂的天气数据\n"
     ]
    },
    {
     "ename": "ConnectionError",
     "evalue": "HTTPConnectionPool(host='www.tianqihoubao.com', port=80): Max retries exceeded with url: /lishi/linyi/month/202111.html (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x00000234D0C5CD48>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed'))",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    156\u001b[0m             conn = connection.create_connection(\n\u001b[1;32m--> 157\u001b[1;33m                 \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dns_host\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mport\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mextra_kw\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    158\u001b[0m             )\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSOCK_STREAM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\socket.py\u001b[0m in \u001b[0;36mgetaddrinfo\u001b[1;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[0;32m    751\u001b[0m     \u001b[0maddrlist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 752\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_socket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    753\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mgaierror\u001b[0m: [Errno 11001] getaddrinfo failed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mNewConnectionError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    671\u001b[0m                 \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 672\u001b[1;33m                 \u001b[0mchunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchunked\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    673\u001b[0m             )\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    386\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 387\u001b[1;33m             \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mhttplib_request_kw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    388\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1251\u001b[0m         \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1252\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1253\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1297\u001b[0m             \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1298\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1299\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1246\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1247\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1025\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1026\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1027\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\http\\client.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    965\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 966\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    967\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    183\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 184\u001b[1;33m         \u001b[0mconn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    185\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    168\u001b[0m             raise NewConnectionError(\n\u001b[1;32m--> 169\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Failed to establish a new connection: %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    170\u001b[0m             )\n",
      "\u001b[1;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0x00000234D0C5CD48>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    448\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 449\u001b[1;33m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    450\u001b[0m                 )\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    719\u001b[0m             retries = retries.increment(\n\u001b[1;32m--> 720\u001b[1;33m                 \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_pool\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    721\u001b[0m             )\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    435\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 436\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    437\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='www.tianqihoubao.com', port=80): Max retries exceeded with url: /lishi/linyi/month/202111.html (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x00000234D0C5CD48>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-a77523564343>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     71\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'__main__'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m     \u001b[0mframe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-1-a77523564343>\u001b[0m in \u001b[0;36mmain\u001b[1;34m()\u001b[0m\n\u001b[0;32m     63\u001b[0m         \u001b[0mmonth_str\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'0'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmonth\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmonth\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m10\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmonth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m         \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'http://www.tianqihoubao.com/lishi/linyi/month/2021'\u001b[0m\u001b[1;33m+\u001b[0m \u001b[0mmonth_str\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'.html'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m         \u001b[0mh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_html\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     66\u001b[0m         \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparse_html\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m     \u001b[0mframe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'date'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'low_tp'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'high_tp'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'weather'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wind'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-1-a77523564343>\u001b[0m in \u001b[0;36mget_html\u001b[1;34m(url)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;31m# 这个网站第一次请求一般都会被反爬给挡住，所以要多请求几次\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'从'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'获取数据'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m'table'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 75\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'get'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 60\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    531\u001b[0m         }\n\u001b[0;32m    532\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 533\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    534\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    535\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    645\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 646\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    647\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mSSLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    517\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mClosedPoolError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectionError\u001b[0m: HTTPConnectionPool(host='www.tianqihoubao.com', port=80): Max retries exceeded with url: /lishi/linyi/month/202111.html (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x00000234D0C5CD48>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed'))"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import re\n",
    "import requests\n",
    "import time\n",
    "from bs4 import BeautifulSoup\n",
    " \n",
    "headers = {\n",
    "            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36'\n",
    "}\n",
    " \n",
    "def get_html(url):\n",
    "# 这个网站第一次请求一般都会被反爬给挡住，所以要多请求几次\n",
    "    while True:\n",
    "        r = requests.get(url, headers=headers)\n",
    "        print('从', url, '获取数据')\n",
    "        if 'table' in r.text:\n",
    "            print('成功获取数据')\n",
    "            return r.content\n",
    "        else:\n",
    "            print('请求失败数据')\n",
    "            time.sleep(1)\n",
    " \n",
    " \n",
    "def parse_html(page_content):\n",
    "    soup = BeautifulSoup(page_content, features='lxml')\n",
    "    table = soup.find('table')\n",
    "    item_list = table.find_all('tr')\n",
    " \n",
    "    month = []\n",
    " \n",
    "    for i in range(1, len(item_list)):\n",
    "        td = item_list[i].find_all('td')\n",
    "        day = list()\n",
    " \n",
    "        # 日期\n",
    "        day.append(parse_date(td[0].a.getText()))\n",
    " \n",
    "        # 高温低温\n",
    "        nums = re.findall(r'-?\\d+', td[2].getText())\n",
    "        day.append(int(nums[1]))\n",
    "        day.append(int(nums[0]))\n",
    " \n",
    "        # 天气和风向\n",
    "        pattern = re.compile(r'\\s+')\n",
    "        day.append(re.sub(pattern, '', td[1].getText()))\n",
    "        day.append(re.sub(pattern, '', td[3].getText()))\n",
    " \n",
    "        month.append(day)\n",
    " \n",
    "    return month\n",
    " \n",
    "def parse_date(text):\n",
    "    y = text.find('年')\n",
    "    m = text.find('月')\n",
    "    d = text.find('日')\n",
    "    return datetime.date(int(text[y - 4: y]), int(text[m - 2: m]), int(text[d - 2: d]))\n",
    " \n",
    "def main():\n",
    "    data = []\n",
    "    for month in range(11, 12):\n",
    "        print(f'爬取2021年{month}月临沂的天气数据')\n",
    "        month_str = '0' + str(month) if month < 10 else str(month)\n",
    "        url = 'http://www.tianqihoubao.com/lishi/linyi/month/2021'+ month_str + '.html'\n",
    "        h = get_html(url)\n",
    "        data.extend(parse_html(h))\n",
    "    frame = pd.DataFrame(data, columns=['date', 'low_tp', 'high_tp', 'weather', 'wind'])\n",
    "    print(frame)\n",
    "    #frame.to_csv('./data/weather.txt', index=False) \n",
    "    return frame\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    frame = main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>low_tp</th>\n",
       "      <th>high_tp</th>\n",
       "      <th>weather</th>\n",
       "      <th>wind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>晴/多云</td>\n",
       "      <td>东北风3-4级/东北风3-4级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-11-02</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>阴/小雨</td>\n",
       "      <td>南风1-2级/南风1-2级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-11-03</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>多云/晴</td>\n",
       "      <td>西南风1-2级/西南风1-2级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-11-04</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>多云/阴</td>\n",
       "      <td>南风1-2级/南风1-2级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-11-05</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>小雨/小雨</td>\n",
       "      <td>东南风3-4级/东南风3-4级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-11-06</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>小雨/阴</td>\n",
       "      <td>东风3-4级/东风3-4级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-11-07</td>\n",
       "      <td>-2</td>\n",
       "      <td>16</td>\n",
       "      <td>中雨/多云</td>\n",
       "      <td>西北风4-5级/西北风4-5级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-11-08</td>\n",
       "      <td>-1</td>\n",
       "      <td>6</td>\n",
       "      <td>晴/晴</td>\n",
       "      <td>西风3-4级/西风3-4级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-11-09</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>晴/晴</td>\n",
       "      <td>西风3-4级/西风3-4级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-11-10</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>晴/晴</td>\n",
       "      <td>西风1-2级/西风1-2级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-11-11</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>晴/晴</td>\n",
       "      <td>西北风1-2级/西北风1-2级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-11-12</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>晴/晴</td>\n",
       "      <td>西风1-2级/西风1-2级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-11-13</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>晴/晴</td>\n",
       "      <td>西南风1-2级/西南风1-2级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-11-14</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>晴/多云</td>\n",
       "      <td>西南风1-2级/西南风1-2级</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-11-15</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>多云/晴</td>\n",
       "      <td>西南风3-4级/西南风3-4级</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  low_tp  high_tp weather             wind\n",
       "0   2021-11-01       6       16    晴/多云  东北风3-4级/东北风3-4级\n",
       "1   2021-11-02      10       17    阴/小雨    南风1-2级/南风1-2级\n",
       "2   2021-11-03      10       19    多云/晴  西南风1-2级/西南风1-2级\n",
       "3   2021-11-04      11       20    多云/阴    南风1-2级/南风1-2级\n",
       "4   2021-11-05      14       19   小雨/小雨  东南风3-4级/东南风3-4级\n",
       "5   2021-11-06      14       19    小雨/阴    东风3-4级/东风3-4级\n",
       "6   2021-11-07      -2       16   中雨/多云  西北风4-5级/西北风4-5级\n",
       "7   2021-11-08      -1        6     晴/晴    西风3-4级/西风3-4级\n",
       "8   2021-11-09       1       10     晴/晴    西风3-4级/西风3-4级\n",
       "9   2021-11-10       3       13     晴/晴    西风1-2级/西风1-2级\n",
       "10  2021-11-11       1       13     晴/晴  西北风1-2级/西北风1-2级\n",
       "11  2021-11-12       2       16     晴/晴    西风1-2级/西风1-2级\n",
       "12  2021-11-13       3       16     晴/晴  西南风1-2级/西南风1-2级\n",
       "13  2021-11-14       5       17    晴/多云  西南风1-2级/西南风1-2级\n",
       "14  2021-11-15       4       17    多云/晴  西南风3-4级/西南风3-4级"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['low_tp'][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frame.plot(x='date',y=['low_tp','high_tp'],figsize=(10,3),kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "str5 = '{\"SBBM\":\"06M00001200397653\",\"result\":{\"msg\":\"success\",\"exception\":[],\"code\":200,\"Data\":{\"ycsb\":\"202106120042GF\",\"dlszd\":\"0.00\",\"vol_b\":\"232.60\",\"vol_c\":\"234.10\",\"vol_a\":\"231.90\",\"gfrl\":\"21.1\",\"cur_b\":\"0.0\",\"gdfd\":\"0\",\"cur_a\":\"0.0\",\"curStatus\":\"1\",\"hgl\":\"0.84\",\"data_date\":\"2021-11-05T15:15:00.000Z\",\"cur_c\":\"0.0\",\"glys\":\"1.00\",\"p\":\"0.00\",\"q\":\"0.00\",\"dyszd\":\"1.72\",\"drfdl\":37.55,\"sxbph\":0,\"szgl\":\"0.00\",\"xnfs\":\"全额上网\",\"gflx\":\"三相\",\"yhmc\":\"包培国\",\"ydzt\":\"用电\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "str6 = '{\"SBBM\":\"06M00001200397653\",\"result\":{\"msg\":\"success\",\"exception\":[],\"code\":200,\"Data\":{\"ycsb\":\"202106120042GF\",\"dlszd\":\"0.00\",\"vol_b\":\"232.40\",\"vol_c\":\"234.10\",\"vol_a\":\"232.30\",\"gfrl\":\"21.1\",\"cur_b\":\"0.0\",\"gdfd\":\"0\",\"cur_a\":\"0.0\",\"curStatus\":\"1\",\"hgl\":\"0.88\",\"data_date\":\"2021-11-06T15:00:00.000Z\",\"cur_c\":\"0.0\",\"glys\":\"1.00\",\"p\":\"0.00\",\"q\":\"0.00\",\"dyszd\":\"1.86\",\"drfdl\":44.9,\"sxbph\":0,\"szgl\":\"0.00\",\"xnfs\":\"全额上网\",\"gflx\":\"三相\",\"yhmc\":\"包培国\",\"ydzt\":\"用电\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "str7 = '{\"SBBM\":\"06M00001200397653\",\"result\":{\"msg\":\"success\",\"exception\":[],\"code\":200,\"Data\":{\"ycsb\":\"202106120042GF\",\"dlszd\":\"0.00\",\"vol_b\":\"234.70\",\"vol_c\":\"236.30\",\"vol_a\":\"234.70\",\"gfrl\":\"21.1\",\"cur_b\":\"0.0\",\"gdfd\":\"0\",\"cur_a\":\"0.0\",\"curStatus\":\"1\",\"hgl\":\"0.94\",\"data_date\":\"2021-11-07T14:40:00.000Z\",\"cur_c\":\"0.0\",\"glys\":\"1.00\",\"p\":\"0.00\",\"q\":\"0.00\",\"dyszd\":\"1.95\",\"drfdl\":0,\"sxbph\":0,\"szgl\":\"0.00\",\"xnfs\":\"全额上网\",\"gflx\":\"三相\",\"yhmc\":\"包培国\",\"ydzt\":\"用电\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "str8 = '{\"SBBM\":\"06M00001200397653\",\"result\":{\"msg\":\"success\",\"exception\":[],\"code\":200,\"Data\":{\"ycsb\":\"202106120042GF\",\"dlszd\":\"0.00\",\"vol_b\":\"236.60\",\"vol_c\":\"240.50\",\"vol_a\":\"239.10\",\"gfrl\":\"21.1\",\"cur_b\":\"0.0\",\"gdfd\":\"0\",\"cur_a\":\"0.0\",\"curStatus\":\"1\",\"hgl\":\"0.69\",\"data_date\":\"2021-11-08T15:00:00.000Z\",\"cur_c\":\"0.0\",\"glys\":\"1.00\",\"p\":\"0.00\",\"q\":\"0.00\",\"dyszd\":\"2.24\",\"drfdl\":4.3,\"sxbph\":0,\"szgl\":\"0.00\",\"xnfs\":\"全额上网\",\"gflx\":\"三相\",\"yhmc\":\"包培国\",\"ydzt\":\"用电\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "str9 = '{\"SBBM\":\"06M00001200397653\",\"result\":{\"msg\":\"success\",\"exception\":[],\"code\":200,\"Data\":{\"ycsb\":\"202106120042GF\",\"dlszd\":\"0.00\",\"vol_b\":\"236.80\",\"vol_c\":\"235.80\",\"vol_a\":\"234.70\",\"gfrl\":\"21.1\",\"cur_b\":\"0.0\",\"gdfd\":\"0\",\"cur_a\":\"0.0\",\"curStatus\":\"1\",\"hgl\":\"0.20\",\"data_date\":\"2021-11-09T15:00:00.000Z\",\"cur_c\":\"0.0\",\"glys\":\"1.00\",\"p\":\"0.00\",\"q\":\"0.00\",\"dyszd\":\"1.76\",\"drfdl\":294.42,\"sxbph\":0,\"szgl\":\"0.00\",\"xnfs\":\"全额上网\",\"gflx\":\"三相\",\"yhmc\":\"包培国\",\"ydzt\":\"用电\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "str10 = '{\"SBBM\":\"06M00001200397653\",\"result\":{\"msg\":\"success\",\"exception\":[],\"code\":200,\"Data\":{\"ycsb\":\"202106120042GF\",\"dlszd\":\"0.00\",\"vol_b\":\"239.30\",\"vol_c\":\"242.30\",\"vol_a\":\"239.50\",\"gfrl\":\"21.1\",\"cur_b\":\"0.0\",\"gdfd\":\"0\",\"cur_a\":\"0.0\",\"curStatus\":\"1\",\"hgl\":\"0.29\",\"data_date\":\"2021-11-10T12:05:00.000Z\",\"cur_c\":\"0.0\",\"glys\":\"1.00\",\"p\":\"0.00\",\"q\":\"0.00\",\"dyszd\":\"2.44\",\"drfdl\":289.32,\"sxbph\":0,\"szgl\":\"0.00\",\"xnfs\":\"全额上网\",\"gflx\":\"三相\",\"yhmc\":\"包培国\",\"ydzt\":\"用电\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "str11 = '{\"SBBM\":\"06M00001200397653\",\"result\":{\"msg\":\"success\",\"exception\":[],\"code\":200,\"Data\":{\"ycsb\":\"202106120042GF\",\"dlszd\":\"0.00\",\"vol_b\":\"231.30\",\"vol_c\":\"235.50\",\"vol_a\":\"236.70\",\"gfrl\":\"21.1\",\"cur_b\":\"0.34\",\"gdfd\":\"0\",\"cur_a\":\"0.34\",\"curStatus\":\"1\",\"hgl\":\"0.32\",\"data_date\":\"2021-11-11T10:00:00.000Z\",\"cur_c\":\"0.34\",\"glys\":\"1.00\",\"p\":\"0.00\",\"q\":\"0.00\",\"dyszd\":\"2.28\",\"drfdl\":300.39,\"sxbph\":0,\"szgl\":\"0.00\",\"xnfs\":\"全额上网\",\"gflx\":\"三相\",\"yhmc\":\"包培国\",\"ydzt\":\"发电\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "str = str5+str6+str7+str8+str9+str10+str11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总用户数: 7\n"
     ]
    }
   ],
   "source": [
    "str = str[0:-1]\n",
    "datedata = '{\"res\":[' + str + ']}'\n",
    "datedata = json.loads(datedata)\n",
    "print(\"总用户数:\",len(datedata['res']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "gfdata = []\n",
    "index = 1\n",
    "for i in range(len(datedata['res'])):\n",
    "    data = dict()\n",
    "    data['sbbm'] = datedata['res'][i]['SBBM']\n",
    "    data['ycsb'] = datedata['res'][i]['result']['Data']['ycsb']\n",
    "    data['yhmc'] = datedata['res'][i]['result']['Data']['yhmc']\n",
    "    \n",
    "    data['p'] = (-1)*float(datedata['res'][i]['result']['Data']['p']) if datedata['res'][i]['result']['Data']['p']!=None else 0\n",
    "    data['q'] = float(datedata['res'][i]['result']['Data']['q']) if datedata['res'][i]['result']['Data']['q']!=None else 0\n",
    "    data['szgl'] = (2)*float(datedata['res'][i]['result']['Data']['szgl']) if datedata['res'][i]['result']['Data']['szgl']!=None else 0\n",
    "    \n",
    "    \n",
    "    data['glys'] = datedata['res'][i]['result']['Data']['glys']\n",
    "    data['gfrl'] = float(datedata['res'][i]['result']['Data']['gfrl']) if datedata['res'][i]['result']['Data']['gfrl']!=None else 0\n",
    "    data['drfdl'] =float(datedata['res'][i]['result']['Data']['drfdl']) if datedata['res'][i]['result']['Data']['drfdl']!=None else 0\n",
    "    \n",
    "    data['id'] = index\n",
    "    data['date'] = datedata['res'][i]['result']['Data']['data_date']\n",
    "    data['low'] = frame['low_tp'][4+i]\n",
    "    data['high'] = frame['high_tp'][4+i]\n",
    "    data['avg'] = (frame['high_tp'][4+i]+frame['low_tp'][4+i])*10\n",
    "    \n",
    "    index = index + 1\n",
    "    gfdata.append(data)\n",
    "        \n",
    "#转为dataframe\n",
    "gfdata = pd.DataFrame(gfdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sbbm</th>\n",
       "      <th>ycsb</th>\n",
       "      <th>yhmc</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>szgl</th>\n",
       "      <th>glys</th>\n",
       "      <th>gfrl</th>\n",
       "      <th>drfdl</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>37.55</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-11-05T15:15:00.000Z</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>44.90</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-11-06T15:00:00.000Z</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-11-07T14:40:00.000Z</td>\n",
       "      <td>-2</td>\n",
       "      <td>16</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>4.30</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-11-08T15:00:00.000Z</td>\n",
       "      <td>-1</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>294.42</td>\n",
       "      <td>5</td>\n",
       "      <td>2021-11-09T15:00:00.000Z</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>289.32</td>\n",
       "      <td>6</td>\n",
       "      <td>2021-11-10T12:05:00.000Z</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>06M00001200397653</td>\n",
       "      <td>202106120042GF</td>\n",
       "      <td>包培国</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.1</td>\n",
       "      <td>300.39</td>\n",
       "      <td>7</td>\n",
       "      <td>2021-11-11T10:00:00.000Z</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sbbm            ycsb yhmc    p    q  szgl  glys  gfrl   drfdl  \\\n",
       "0  06M00001200397653  202106120042GF  包培国 -0.0  0.0   0.0  1.00  21.1   37.55   \n",
       "1  06M00001200397653  202106120042GF  包培国 -0.0  0.0   0.0  1.00  21.1   44.90   \n",
       "2  06M00001200397653  202106120042GF  包培国 -0.0  0.0   0.0  1.00  21.1    0.00   \n",
       "3  06M00001200397653  202106120042GF  包培国 -0.0  0.0   0.0  1.00  21.1    4.30   \n",
       "4  06M00001200397653  202106120042GF  包培国 -0.0  0.0   0.0  1.00  21.1  294.42   \n",
       "5  06M00001200397653  202106120042GF  包培国 -0.0  0.0   0.0  1.00  21.1  289.32   \n",
       "6  06M00001200397653  202106120042GF  包培国 -0.0  0.0   0.0  1.00  21.1  300.39   \n",
       "\n",
       "   id                      date  low  high  avg  \n",
       "0   1  2021-11-05T15:15:00.000Z   14    19  330  \n",
       "1   2  2021-11-06T15:00:00.000Z   14    19  330  \n",
       "2   3  2021-11-07T14:40:00.000Z   -2    16  140  \n",
       "3   4  2021-11-08T15:00:00.000Z   -1     6   50  \n",
       "4   5  2021-11-09T15:00:00.000Z    1    10  110  \n",
       "5   6  2021-11-10T12:05:00.000Z    3    13  160  \n",
       "6   7  2021-11-11T10:00:00.000Z    1    13  140  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gfdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='id'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfdata.plot(x='id',y=['avg','drfdl'],figsize=(10,3),kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 市数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "c5 = '{\"SSDS\":\"37413\",\"Date\":\"2021-11-05T15:10:00.000Z\",\"result\":{\"msg\":\"success\",\"code\":200,\"Data\":{\"kgdz\":\"0\",\"drfdzl\":\"612953.34\",\"gfbhxhcs\":\"0\",\"ycgj\":\"0\",\"gfstl\":\"#.######\",\"xb\":\"196\",\"gdyhs\":\"131\",\"gfzrl\":\"606318989209\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "c6 = '{\"SSDS\":\"37413\",\"Date\":\"2021-11-06T14:55:00.000Z\",\"result\":{\"msg\":\"success\",\"code\":200,\"Data\":{\"kgdz\":\"0\",\"drfdzl\":\"507197.60\",\"gfbhxhcs\":\"0\",\"ycgj\":\"0\",\"gfstl\":\"#.######\",\"xb\":\"218\",\"gdyhs\":\"127\",\"gfzrl\":\"606318989153\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "c7 = '{\"SSDS\":\"37413\",\"Date\":\"2021-11-07T14:40:00.000Z\",\"result\":{\"msg\":\"success\",\"code\":200,\"Data\":{\"kgdz\":\"0\",\"drfdzl\":\"-21030.66\",\"gfbhxhcs\":\"0\",\"ycgj\":\"0\",\"gfstl\":\"#.######\",\"xb\":\"156\",\"gdyhs\":\"158\",\"gfzrl\":\"606318989153\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "c8 = '{\"SSDS\":\"37413\",\"Date\":\"2021-11-08T15:00:00.000Z\",\"result\":{\"msg\":\"success\",\"code\":200,\"Data\":{\"kgdz\":\"0\",\"drfdzl\":\"2058243.77\",\"gfbhxhcs\":\"0\",\"ycgj\":\"0\",\"gfstl\":\"#.######\",\"xb\":\"198\",\"gdyhs\":\"224\",\"gfzrl\":\"606318989153\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "c9  = '{\"SSDS\":\"37413\",\"Date\":\"2021-11-09T15:00:00.000Z\",\"result\":{\"msg\":\"success\",\"code\":200,\"Data\":{\"kgdz\":\"0\",\"drfdzl\":\"2192164.71\",\"gfbhxhcs\":\"0\",\"ycgj\":\"0\",\"gfstl\":\"#.######\",\"xb\":\"207\",\"gdyhs\":\"178\",\"gfzrl\":\"606318989153\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "c10 = '{\"SSDS\":\"37413\",\"Date\":\"2021-11-10T11:25:00.000Z\",\"result\":{\"msg\":\"success\",\"code\":200,\"Data\":{\"kgdz\":\"0\",\"drfdzl\":\"2566333.31\",\"gfbhxhcs\":\"0\",\"ycgj\":\"0\",\"gfstl\":\"#.######\",\"xb\":\"483\",\"gdyhs\":\"266\",\"gfzrl\":\"606318989153\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "c11 = '{\"SSDS\":\"37413\",\"Date\":\"2021-11-11T10:00:00.000Z\",\"result\":{\"msg\":\"success\",\"code\":200,\"Data\":{\"kgdz\":\"0\",\"drfdzl\":\"2284516.38\",\"gfbhxhcs\":\"0\",\"ycgj\":\"0\",\"gfstl\":\"#.######\",\"xb\":\"329\",\"gdyhs\":\"188\",\"gfzrl\":\"606318989153\"}}},'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = c5+c6+c7+c8+c9+c10+c11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总数: 7\n"
     ]
    }
   ],
   "source": [
    "str = c[0:-1]\n",
    "datedata = '{\"res\":[' + str + ']}'\n",
    "datedata = json.loads(datedata)\n",
    "print(\"总数:\",len(datedata['res']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "gfdata = []\n",
    "index = 1\n",
    "for i in range(len(datedata['res'])):\n",
    "    data = dict()\n",
    "    data['date'] = datedata['res'][i]['Date']\n",
    "    data['drfdzl'] = float(datedata['res'][i]['result']['Data']['drfdzl'])\n",
    "\n",
    "    data['low'] = frame['low_tp'][4+i]\n",
    "    data['high'] = frame['high_tp'][4+i]\n",
    "    data['avg'] = (frame['high_tp'][4+i]+frame['low_tp'][4+i])*100000\n",
    "    data['id'] = index\n",
    "    index = index + 1\n",
    "    gfdata.append(data)\n",
    "        \n",
    "#转为dataframe\n",
    "gfdata = pd.DataFrame(gfdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>drfdzl</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>avg</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-11-05T15:10:00.000Z</td>\n",
       "      <td>612953.34</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>3300000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-11-06T14:55:00.000Z</td>\n",
       "      <td>507197.60</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>3300000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-11-07T14:40:00.000Z</td>\n",
       "      <td>-21030.66</td>\n",
       "      <td>-2</td>\n",
       "      <td>16</td>\n",
       "      <td>1400000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-11-08T15:00:00.000Z</td>\n",
       "      <td>2058243.77</td>\n",
       "      <td>-1</td>\n",
       "      <td>6</td>\n",
       "      <td>500000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-11-09T15:00:00.000Z</td>\n",
       "      <td>2192164.71</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1100000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-11-10T11:25:00.000Z</td>\n",
       "      <td>2566333.31</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>1600000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-11-11T10:00:00.000Z</td>\n",
       "      <td>2284516.38</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1400000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       date      drfdzl  low  high      avg  id\n",
       "0  2021-11-05T15:10:00.000Z   612953.34   14    19  3300000   1\n",
       "1  2021-11-06T14:55:00.000Z   507197.60   14    19  3300000   2\n",
       "2  2021-11-07T14:40:00.000Z   -21030.66   -2    16  1400000   3\n",
       "3  2021-11-08T15:00:00.000Z  2058243.77   -1     6   500000   4\n",
       "4  2021-11-09T15:00:00.000Z  2192164.71    1    10  1100000   5\n",
       "5  2021-11-10T11:25:00.000Z  2566333.31    3    13  1600000   6\n",
       "6  2021-11-11T10:00:00.000Z  2284516.38    1    13  1400000   7"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gfdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='id'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gfdata.plot(x='id',y=['avg','drfdzl'],figsize=(10,3),kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 未来天气数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    日期    \t   天气   \t  最高温度  \t  最低温度  \t   风级   \n",
      " 18日（今天）  \t  晴转多云  \t   19   \t   6    \t3-4级转<3级\n",
      " 19日（明天）  \t  晴转多云  \t   19   \t   6    \t  <3级   \n",
      " 20日（后天）  \t  阴转小雨  \t   16   \t   9    \t  <3级   \n",
      " 21日（周日）  \t 小雨转多云  \t   14   \t   2    \t  3-4级  \n",
      " 22日（周一）  \t   晴    \t   7    \t  -3    \t3-4级转<3级\n",
      " 23日（周二）  \t   晴    \t   9    \t  -2    \t3-4级转<3级\n",
      " 24日（周三）  \t   晴    \t   12   \t   1    \t  <3级   \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests                                                                      \n",
    "from bs4 import BeautifulSoup \n",
    "import pandas as pd\n",
    "\n",
    "def getHTMLText(url,timeout = 30):\n",
    "    try:\n",
    "        r = requests.get(url, timeout = 30)       #用requests抓取网页信息\n",
    "        r.raise_for_status()                      #可以让程序产生异常时停止程序\n",
    "        r.encoding = r.apparent_encoding\n",
    "        return r.text\n",
    "    except:\n",
    "        return '产生异常'\n",
    "    \n",
    "def get_data(html):\n",
    "    final_list = []\n",
    "    soup = BeautifulSoup(html,'html.parser')       #用BeautifulSoup库解析网页\n",
    "    body  = soup.body\n",
    "    data = body.find('div',{'id':'7d'})\n",
    "    ul = data.find('ul')\n",
    "    lis = ul.find_all('li')\n",
    " \n",
    "    for day in lis:\n",
    "        temp_list = []\n",
    "        \n",
    "        date = day.find('h1').string             #找到日期\n",
    "        temp_list.append(date)     \n",
    "    \n",
    "        info = day.find_all('p')                 #找到所有的p标签\n",
    "        temp_list.append(info[0].string)\n",
    "    \n",
    "        if info[1].find('span') is None:          #找到p标签中的第二个值'span'标签——最高温度\n",
    "            temperature_highest = ' '             #用一个判断是否有最高温度\n",
    "        else:\n",
    "            temperature_highest = info[1].find('span').string\n",
    "            temperature_highest = temperature_highest.replace('℃',' ')\n",
    "            \n",
    "        if info[1].find('i') is None:              #找到p标签中的第二个值'i'标签——最高温度\n",
    "            temperature_lowest = ' '               #用一个判断是否有最低温度\n",
    "        else:\n",
    "            temperature_lowest = info[1].find('i').string\n",
    "            temperature_lowest = temperature_lowest.replace('℃',' ')\n",
    "            \n",
    "        temp_list.append(temperature_highest)       #将最高气温添加到temp_list中\n",
    "        temp_list.append(temperature_lowest)        #将最低气温添加到temp_list中\n",
    "    \n",
    "        wind_scale = info[2].find('i').string      #找到p标签的第三个值'i'标签——风级，添加到temp_list中\n",
    "        temp_list.append(wind_scale)\n",
    "    \n",
    "        final_list.append(temp_list)              #将temp_list列表添加到final_list列表中\n",
    "    return final_list\n",
    "    \n",
    "#用format()将结果打印输出\n",
    "def print_data(final_list,num):\n",
    "    print(\"{:^10}\\t{:^8}\\t{:^8}\\t{:^8}\\t{:^8}\".format('日期','天气','最高温度','最低温度','风级'))\n",
    "    for i in range(num):    \n",
    "        final = final_list[i]\n",
    "        print(\"{:^10}\\t{:^8}\\t{:^8}\\t{:^8}\\t{:^8}\".format(final[0],final[1],final[2],final[3],final[4]))\n",
    "        \n",
    "#用main()主函数将模块连接\n",
    "def main():\n",
    "    url = 'http://www.weather.com.cn/weather/101120901.shtml'\n",
    "    html = getHTMLText(url)\n",
    "    final_list = get_data(html)\n",
    "    print_data(final_list,7)\n",
    "main()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
